Search results for "Information fusion"

Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection

2008

The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detectio…

Automated Uncertainty Quantification Through Information Fusion in Manufacturing Processes

2017

International audience; Evaluation of key performance indicators (KPIs) such as energy consumption is essential for decision-making during the design and operation of smart manufacturing systems. The measurements of KPIs are strongly affected by several uncertainty sources such as input material uncertainty, the inherent variability in the manufacturing process, model uncertainty, and the uncertainty in the sensor measurements of operational data. A comprehensive understanding of the uncertainty sources and their effect on the KPIs is required to make the manufacturing processes more efficient. Towards this objective, this paper proposed an automated methodology to generate a hierarchical B…

Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources

2020

This paper reviews the most important information fusion data-driven algorithms based on Machine Learning (ML) techniques for problems in Earth observation. Nowadays we observe and model the Earth with a wealth of observations, from a plethora of different sensors, measuring states, fluxes, processes and variables, at unprecedented spatial and temporal resolutions. Earth observation is well equipped with remote sensing systems, mounted on satellites and airborne platforms, but it also involves in-situ observations, numerical models and social media data streams, among other data sources. Data-driven approaches, and ML techniques in particular, are the natural choice to extract significant i…

Distributed Systems for Fusion of Visual Information

1997

Toward an Integrated System for Surveillance and Behaviour Analysis of Groups and People

2013

Security and INTelligence SYStem is an Italian research project which aims to create an integrated system for the analysis of multi-modal data sources (text, images, video, audio), to assist operators in homeland security applications. Within this project the Scientific Research Unit of the University of Palermo is responsible of the image and video analysis activity. The SRU of Palermo developed a web service based architecture that provides image and video analysis capabilities to the integrated analysis system. The developed architecture uses both state of the art techniques, adapted to cope with the particular problem at hand, and new algorithms to provide the following services: image …

Influence Assessment in Twitter Multi-relational Network

2015

International audience; Influence in Twitter has become recently a hot research topic since this micro-blogging service is widely used to share and disseminate information. Some users are more able than others to influence and persuade peers. Thus, studying most influential users leads to reach a large-scale information diffusion area, something very useful in marketing or political campaigns. In this paper, we propose a new approach for influence assessment on Twitter network, it is based on a modified version of the conjunctive combination rule in belief functions theory in order to combine different influence markers such as retweets, mentions and replies. We experiment the proposed meth…

M-VIF: A machine-vision based on information fusion

2002

The authors describe a new architecture for machine vision, which is based on information fusion approach. Its general design has been developed by using a formal computation model that integrates three main ingredients of the visual computation: the data, the models, and the algorithms. The hardware design and the software environment of M-VIF are also given. The simulation of M-VIF is under development on the HERMIA-machine.

Mimicking biological mechanisms for sensory information fusion

2013

Current Artificial Intelligence systems are bound to become increasingly interconnected to their surrounding environment in the view of the newly rising Ambient Intelligence (AmI) perspective. In this paper, we present a comprehensive AmI framework for performing fusion of raw data, perceived by sensors of different nature, in order to extract higher-level information according to a model structured so as to resemble the perceptual signal processing occurring in the human nervous system. Following the guidelines of the greater BICA challenge, we selected the specific task of user presence detection in a locality of the system as a representative application clarifying the potentialities of …

An Integrated Method for Image Retrieval

1995

This paper presents an information fusion method for image retrieval; the retrieval strategy is based on statistical and geometrical features extracted from sub-images. Our goal is to find a set of best similar images related to a prototype image; this goal may be obtained according to image content rather than symbolic attributes. MRI (Magnetic Resonance Imaging) images and astronomical images have been adopted to test the method. Main steps of the procedure to retrieve images are: (l) Segmentation, (2) Matching, and (3) Decision. The first step involves four clusters co-operating segmentation algorithms; in the matching step, a sets of candidate similar images is provided; finally an info…

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

2015

Collaborative Filtering (CF) is one of the most successful algorithms in recommender systems. However, it suffers from data sparsity and scalability problems. Although many clustering techniques have been incorporated to alleviate these two problems, most of them fail to achieve further significant improvement in recommendation accuracy. First of all, most of them assume each user or item belongs to a single cluster. Since usually users can hold multiple interests and items may belong to multiple categories, it is more reasonable to assume that users and items can join multiple clusters (groups), where each cluster is a subset of like-minded users and items they prefer. Furthermore, most of…